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This is the pre-peer-reviewed version of the following article:

Gerlitz, Carolin, and Anne Helmond. 2013. “The Like Economy: Social Buttons and

the Data-intensive Web.” New Media & Society (February 4).

which has been published in final form with doi:10.1177/1461444812472322 and is

available in New Media & Society's Online First:

http://nms.sagepub.com/content/early/2013/02/03/1461444812472322

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The Like economy – Social buttons and the data-intensive web

Carolin Gerlitz and Anne Helmond, University of Amsterdam

This is a pre-review manuscript. The full paper will be published in: New Media &

Society, 2013.

Abstract

The paper examines Facebook’s ambition to extend into the entire web by focusing on

social buttons, developing a medium-specific platform critique. It contextualises the

rise of buttons and counters as metrics for user engagement of different web

economies to show that Facebook is not only creating a social web but a data-

intensive fabric - the Like economy. The implementation of Like buttons enables data

flows between the platform and external websites that enter multiple processes of

exchange and contributes to a simultaneous de- and recentralisation of the web,

advancing Facebook as the central hub. The Like economy instantly metrifies user

engagement and affects into numbers on button counters, which can be traded but also

potentially multiplied and scaled up. Whereas Facebook promotes social buttons as

enablers of a more social web experience, its infrastructure collapses the social with

the traceable and points to Facebook's limits of sociality.

Keywords: Social Web, Facebook, Social Buttons, Like economy, web analytics,

metrification, affect, medium-specificity, digital methods, platform studies

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Introduction

Since April 2010, Facebook has increasingly expanded beyond the limits of its

platform, offering devices that can turn any website and any web user into a part of its

platform. The first step towards this expansion was the introduction of the Open

Graph which allows external websites to link to the platform and its social

connections through external Like and Share buttons. The possibilities to connect

one’s Facebook profile to web objects have even been more expanded after the last F8

developer conference in September 2011 with the introduction of Facebook actions

and objects. Now developers can create apps and buttons that allow users to perform

any custom action on any web object. The idea behind this expansion is to enable

more social web engagement, as Facebook CEO Mark Zuckerberg suggests: ‘making

it so all websites can work together to build a more comprehensive map of

connections and create better, more social experiences for everyone’ (2010). In a later

interview, he takes the promise of sociality even further, claiming that ‘If you look

five years out, every industry is going to be rethought in a social way’ (Gelles, 2010).

In this paper we have examined Facebook’s expansion into the web from a

medium-specific perspective, that is ‘to follow the medium’ and to take its ontological

distinctiveness (Rogers, 2012) seriously by focusing on the role of social buttons and

their increasing implementation. By tracing the buttons and the data flows they

enable, we show how Facebook uses a rhetoric of sociality and connectivity to create

an infrastructure in which social interactivity and user affects are instantly turned into

valuable consumer data. Linking Facebook’s efforts to a historical perspective on the

hit and link economy, we claim that what is in the making is not only a social web,

but a recentralised, data intensive fabric - the Like economy. In this Like economy,

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the social is collapsed with the traceable, as user affects and interactions are instantly

measured for data mining purposes and multiplied in order to generate more traffic

and engagement.

In what follows we first address the emergence of social buttons in relation to

specific web economies, introducing the technical specificity of the Like button, the

Open Graph and Social Plugins. We trace how these features create both data flows

between Facebook and external sites and contribute to a simultaneous de- and

recentralisation of the web, advancing Facebook as the central hub. Further, we

address the capacity of the Like button to instantly metrify and intensify user affects –

turning them into numbers on the Like counter that can potentially be multiplied and

scaled up. Finally, we conclude by arguing that Facebook’s social web is creating an

infrastructure of re-centralised data mining and draws attention to the limits of

sociality in the context of the Like economy.

1. The Informational web: The Hit and Link economy

The Facebook Like button is one of the many social buttons, also referred to as social

bookmarking icons, that have proliferated across the web. These buttons allow users

to share, recommend, like or bookmark content, posts and pages across various social

media platforms such as Facebook, Twitter, Digg, Reddit, Delicious, StumbleUpon

and Google+. They often come with a counter showing how many times the objects

have been shared, recommended, liked or bookmarked across associated platforms.

The emergence of these social buttons and counters can be traced back to the mid

1990s when hit counters showing the number of visitors of a website were common.

In the following section, a genealogical account will introduce buttons and counters as

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metrics of user engagement specific to different web periods and web economies.

In the early days of the informational web, the number of hits was deployed as

one of the first metrics to measure user engagement with a website (D’Alessio, 1997).

The concept of the informational web is used to describe the web as a medium for

publishing content (Ross, 2009), characterised by the linking of information (Wesh,

2007). Hit counters displayed a rough indication of the number of visitors to a

website, derived from the number of computerised requests – hits – to retrieve the

page. Despite this rudimentary quality, hits became the standard for measuring

website traffic (D'Alessio, 1997). Hits advanced to a central indicator for user

engagement and soon served as a key metric for web advertising: the more hits a page

retrieved, the more interesting it became for placing banner adverts. The increasing

centrality of the hit and its exchange value was conceptualised in the notion of the ‘hit

economy’ (Rogers, 2002: 196). While hits cannot be bought or exchanged directly,

websites would buy their way into the top of search engines or onto the front page of

portal pages in order to attract more hits and so be of more interest to advertisers

(Rogers, 2002: 197).

The key metric of the informational web changed in the late 1990s when a

new type of search engine, Google, shifted the value determination of websites from

pure hits to hits and links, adding a qualitative dimension. Inspired by the academic

citation index, Google introduced the link as a recommendation unit on the web and

turned it into the main relevance measure for ranking websites (Page et al., 1998).

Google founders Sergey Brin and Larry Page created the hyperlink analysis algorithm

PageRank, which calculates the relative importance and ranking of a page within a

larger set of pages, based on the number of inlinks to the page and recursively the

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value of the pages linking to it. Google so determined that not all links have equal

value, as links from authoritative sources or links from sources receiving many inlinks

are adding more weight to the algorithm (Gibson et al., 1998).

A high PageRank became a quality indicator of a website, and many websites

displayed their PageRank on their website with a PageRank button. The algorithm

established an economy governed by search engines that regulate the value of each

link (Walker, 2002). In order to increase their PageRank, websites engage in mutual

linking practices, but the central role of the link also gave rise to so-called black

markets of links where reciprocal links are traded in order to improve a site’s ranking.

These markets and link farms create linking schemes between a number of websites

and so thrive on artificial linking dynamics - which are considered bad linking

practices by search engines and are increasingly penalised. But they also contribute to

a commodification of links as web objects that can be traded, sold or bought within

the ‘link economy’ (Rogers, 2002; Walker, 2002).

The move from merely hitting to linking is a first step to including social

validation and relational value in search engine algorithms. However, this social

validation largely remains an expert system, since the value of an inlink is determined

by the degree of the inlinker's authority. The blogosphere has played an important role

in advancing the link economy beyond an expert system. First, the blogosphere re-

introduced the notion of user engagement from the hit economy by taking the number

of subscribers who receive automatic update notifications through site feeds and blog

subscriptions as a quality measure of blogs. Second, the blogosphere gave rise to a

recommendation culture in which bloggers are linking and recommending sources and

are ‘freed from the “tyranny of (old media) editors”’ (Rogers, 2005: 7), thus making

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the practice of linking no longer exclusive to webmasters. Third, the blogosphere

further opened up the act of linking by allowing users to place links in blog

comments. This has led to radical changes in the link economy as Google and other

search engines decided by mutual agreement with blog software providers that those

links would not count in the link economy (Cutts and Shellen, 2005), assigning

different weight to links even within websites. As blog comments are prone to spam,

search engines decided to exclude user generated links from sorting algorithm and

kept holding on to the informational web as expert system.

2. The social web: The Like economy

The social web further developed user-focused web metrics introduced by the

blogosphere and presented them to the entire web. The term social web is used to

describe the shift from a web based on information provision and an expert-system to

a participatory and collaborative production of content and its cross-syndication

across the web (Beer 2009). Different from experts creating links between webpages

in the informational web, the social web forms a set of relations created by users

linking to multiple web objects such as pictures, status updates, profiles or people

(Appelquist et al., 2010). Social buttons are a key feature of the social web and have

profoundly reconfigured the practices of hitting and linking. They also enable new

dynamics of exchange, which shall be conceptualised as the Like economy.

Social buttons emerged around 2005/06 when content aggregation websites

such as Digg and Reddit popularised the acts of sharing and recommending content

from across the web by creating a button that can be placed on any website enabling

users to submit or vote for a post on the related platform. Implemented by webmasters

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across the web, the buttons afford a number of predefined user activities (e.g. voting,

recommending, bookmarking, sharing, tweeting, liking) in relation to their associated

social media platforms. The related button counters show the total number of

predefined activities performed on the object. For publishers the social buttons enable

the cross-syndication of their stories across various platforms. Instead of webmasters

creating an expert system of links between web content, the buttons enable users to

participate in the recommendation culture on the web.

Facebook introduced social buttons with the launch of the share icon in 2006

as a way of sharing content from all over the web with one’s contacts in order to

invoke further social activities on the platform such as resharing, commenting and

later liking (Kinsey, 2009). The icon was complemented with a counter in 2009,

featuring the number of shares generated and tracking the popularity of an item on the

web. Liking and the accompanying Like button were introduced in 2009 and were

presented as a shortcut to commenting in order to replace short affective statements

like ‘Awesome’ and ‘Congrats!’ (Pearlman, 2009). It was put forward as a social

activity that can be performed on most shared objects within Facebook, such as status

updates, photos, links or comments. Liking was initially only available within the

platform and came with a counter showing the total number of likes as well as the

names of friends who clicked it. In 2010, Facebook introduced an external Like

button, which can be implemented by any web master on any website, and potentially

makes all web content likeable. According to Facebook, more than 7 million apps and

websites are integrated with the platform and more than 2 billion posts are liked and

commented on per day (Facebook Statistics, 2011). With the increasing popularity of

the Like button, browser plugins such as the official Like button add-on for Google

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Chrome have been developed, enabling users to like any web content regardless of the

presence of a Like button (Siegler, 2011). But the Like button not only captures actual

likes, it is – just as the Share button – set up as a composite metric which aggregates

all activities performed on an object: the number of both likes and shares of a object,

such as an URL, likes and comments on stories within Facebook about this object and

the number of inbox messages containing this object as an attachment.

The Like button is build on top of both links and hits, while adding an

affective dimension to them. It can be understood as a so-called preconfigured link, as

a click on the button automatically creates a hyperlink between external web content

and the platform. By removing the need to manually copy and paste a URL to share,

sharing is simplified and opened up to users rather than being exclusive to

webmasters, producing a new infrastructure of ‘light‘ and ‘user-generated linking’.

Yet, what is being created is more than just a link, as the Like presents the shared

content as a positive, affective recommendation and enables data flows from the

platform to the external website, incrementing the Like counter and providing

Facebook Analytics, see below. Each like is supposed to generate further engagement

but also traffic to the external websites - therewith also qualifying hits coming from

Facebook as recommendation traffic. In this sense, Facebook has decided to promote

the ‘like’ over the ‘share’ as both provide the same functionality, but the Like button

features content as recommendation rather than a neutral ‘share’ - creating positive

links and positive engagement.

The Like and the Share buttons are part of Facebook’s Open Graph API,

which aims to connect Facebook’s social graph with the entire web. The social graph

refers to the connections users create between each other and poses the term Facebook

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uses to refer to the social network enabled through its platform (Facebook

Developers, 2011). Facebook’s social graph is regulated by the Open Graph Protocol,

which defines how users can create connections with each other or with web objects.

Although it is presented as an open infrastructure, Facebook controls the protocol and

uses proprietary API calls to send and retrieve data and objects from and to

Facebook’s database. The connection between social and Open Graph is mainly

established through so-called Social Plugins that enable data flows between

Facebook, its social graph and external websites. Among the Social Plugins are the

Like button, but also devices such as the Activity Feed featuring all public Facebook

activities related to a topic or the comment box that links external comments to

Facebook profiles. All plugins aim at enabling users to experience the web in relation

to Facebook features, to connect any content or activity to their profiles and to see

what other friends have engaged with.

These devices create an infrastructure in which web users can engage with

potentially all web content outside of the platform through Facebook-based activities

such as liking, sharing or commenting, setting off a number of data flows and

exchange dynamics. Once Facebook users click on Like or Share button on an

external website, this activity is documented on their Facebook walls and appears in

their contacts’ news feeds and/or tickers, while incrementing the Like button counter.

The external web content now becomes available for further liking and commenting

within the Facebook platform, generating additional data flows back to external

counters. More anonymised data is flowing from Facebook to webmasters in the form

of Facebook Analytics which shows button impressions outside and inside the

platform, clicks but also anonymised, basic demographic data of likers such as age,

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gender and location.

But not all contributors and contributions are visible. According to Roosendaal

the Like button functions as a cookie that is activated once a visitor opens a site with

an implemented Like button (2010). From that moment on, the button is tracing the

visitor’s browsing behaviour and is automatically generating data for Facebook by

connecting it to the individual Facebook profiles. Being tracked by Facebook through

such cookies can only be prevented by disabling the use of cookies in the browser

options or by installing a browser add-on that disables third-party tracking such as

Disconnect or Ghostery. However, this does not only apply to Facebook users, the

Like button cookie also traces non-users and adds the information as anonymous data

to the Facebook database. While the browsing data is said to be solely used by

Facebook to enhance its user patterning, Like button impressions are fed back to

webmasters through Facebook Analytics. Therewith the Like button turns any web

user into a potential Facebook user, as each user may unknowingly contribute to the

production of valuable browsing data for the platform.

At its 2011 F8 developer conference, Facebook has expanded the possibilities

of content sharing and invisible participation even further, most notably through the

aforementioned Facebook custom actions. When creating an app, developers are

prompted to define verbs that are shown as user actions and to specify the object on

which these actions can be performed. Instead of being confined to ‘like’ external web

content, users can now ‘read’, ‘watch’, ‘discuss’ or perform other actions. While this

extension of the Open Graph allows Facebook users to perform any action on any

object, it also comes with the highly controversial feature of frictionless sharing that

automatically posts such activities to the ticker, once users have signed up for an app

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(MacManus, 2011). Thereby Facebook’s actions and the new apps differ from the

Like button in many ways. Whereas the button requires an active click to share

content, the new actions enable automatic engagement with and sharing of content.

Also, while recommendations via a Like direct users to websites outside of Facebook,

the new actions only refer to Facebook-internal app content, making users remain

within the platform.

The Open Graph and the external Like buttons create a data-intensive

infrastructure which is characterised by multiple processes of exchange that create

surplus value for a number of actors. Webmasters are granting Facebook real on their

web pages estate ‒ by integrating an iFrame displaying the button ‒ in exchange for

user engagement, platform traffic and user data through Facebook Analytics. Users

are trading their data and affects to enable social interaction with other users and to

perform their online identity. Facebook is opening its walled garden in a controlled

way, letting carefully selected user data flow outside of the platform in order to

maximise data flows inside the platform.

But also third party actors increasingly participate in the Like economy.

Companies and artists have started setting up so-called Likewalls – trading access to

content for a click on the Like of their Facebook Fan page. In order to increase user

engagement, companies can purchase Likers in packages of 1k-100k from around

$50. Such Like farms ‒ named after the link farms of the link economy ‒ supposedly

provide likes from real profiles that help to communicate Facebook pages to their

network of friends. The Like is advancing into a key currency in the emerging Like

economy, allowing for the exchange of multiple entities such as affects, data, traffic,

web space and of course money.

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The Like economy thus builds on key features of previous web economies

such as the hit and the link economy, but introduces a number of changes, most

notably the re-introduction of web users as main agents and the affective qualification

of hitting and linking. In the informational web, Google has relied on expert links

from webmasters and bloggers to create a link economy and at the same time has

removed value from user links in the comments, whereas in the social web Facebook

relies on user-generated links through liking and sharing. In addition, the platform is

based and dependent on both webmasters and bloggers placing social plugins on their

websites to create the infrastructure of the Like economy. At the same time Facebook

also qualifies hits and links as affective recommendations and data-rich activities, as

the plugins enable multiple data flows in the back-end. The Like combines both hits

and links and creates a web economy in which a click on a button generates surplus

for a wide number of actors.

3. The de- and recentralization of the web – The ecology of the Like economy

To explore how the multiple processes of exchange are enabled and how the creation

of surplus for the various actors is regulated, we will now look into the specific

ecology of the Like economy. Facebook has been discussed and criticised as a walled

garden, as a closed infrastructure, limiting connectivity to the web and only promoting

sharing within the network itself (Berners-Lee, 2010). However, with the introduction

of Social Plugins and the Open Graph, Facebook’s activities such as liking,

commenting and sharing are no longer confined to the space of Facebook but they

foster the cross-syndication of content into the platform and enable partial data flows

outside of Facebook. While the introduction of the Open Graph led to a partial

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opening of the walled garden, the introduction of Facebook’s new Open Graph

actions and frictionless sharing are considered a counter-movement: incorporating

even more activities happening outside of the platform and at the same time

encapsulating them into the platform instead of creating controlled outflows of data

(Sabbagh, 2011).

The increasing integration of social buttons into websites and the use of Like

button browser plugins renders both the sites and Facebook more open and less fixed.

Engagement with web content is not confined to designated comment spaces, but

takes place across a wide range of platforms and within Facebook across many

profiles and news feeds. Within this process, external websites cannot be considered

as discrete entities, but function as initialisers for web engagement. The more social

plugins a website integrates, the more it opens itself up to being shaped by the

activities of Facebook users. Users will also experience such websites in a

personalised way, as Social Plugins provide recommendations based on the user’s

contacts and features the engagement of friends with the website. Whereas these are

rather novel perspectives for the web, they are key characteristics of social media

platforms, which have little original content and are shaped by cross-syndication

practices (boyd, 2010). The data flows of the Like economy and its cross-syndication

of content make both Facebook and external websites more relational as activities in

one space will affect the other. This partial opening of the walled garden is also an

incentive for webmasters to participate in the Like economy since the social buttons

provide a new way to get engagement with and traffic to their content. In the

informational web, with its hit and link economy, website traffic is driven by portals,

search engines or referrals from other websites whereas in the social web traffic

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increasingly comes from social media platforms where content is shared and has the

potential of being reshared in a new circle of contacts.

While these dynamics can be understood as a decentralisation of both

Facebook and external websites, as their key features and their contents are

distributed across a wide range of sites, profiles, feeds and tickers, at the same time all

links and data-flows go directly back to Facebook. Especially Facebook’s efforts to

make each and every web experience more social, hence connecting all web

experience to its platform, indicates a simultaneous rewiring of the web. Social

buttons recentralise linking practices as they instantly direct back to the platform as

opposed to the reciprocal linking practices of webmasters. The Like economy is

creating a fabric of the web – an underlying infrastructure – in which Facebook is

increasingly advancing as the central hub of both linking and data flows, regulating to

which extent other actors are allowed to participate and benefit.

The introduction of Open Graph actions and frictionless sharing adds another

quality to the dynamics of recentralisation. First of all, these features integrate

external content even stronger into the platform, as engagement with the web and

mobile services is now promoted via apps rather than external buttons which refer

users to content within the platform as opposed to linking to external websites.

Facebook’s actions allow to differentiate the possibilities of engaging with content.

Users no longer just have to like or to share, but can materialise multiple actions by

using related apps and thus feed back more profile data into the platform. Moreover,

the new apps are not limited to web-native content such as websites and blogposts,

but are open to all possible objects. Hence, data collection from users can be

multiplied far beyond the potentials of the Like button.

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This process is accelerated through frictionless sharing which connects any

action performed with the app instantly to user profiles and posts it to the ticker. Data

collection is thus no longer dependent on rather conscious acts of sharing or liking,

but enables instant and scalable monitoring of its users, recentralising data mining

even further (MacManus, 2011).

A number of actors are contributing to the creation of this fabric of the web,

but not all are given full access to the data they produce themselves. In the case of

external Likes, data flows are first of all directed to Facebook and are then fed back in

a highly controlled way to other actors involved. Until late 2011, users’ Likes and

Shares were merely posted on their walls and not saved centrally. This has changed

with the introduction of the Timeline, the successor of the Facebook profile wall,

which allows to arrange one’s Facebook activities per year or month, enabling users

to turn their profile into a lifetime diary. Instead of just listing likes and shares in an

endless stream, Timeline summarises them in monthly or annual reports. However,

users still cannot directly search and use their Likes as bookmarking system and Likes

partially remain their status as fleeting objects for spontaneous engagement.

Webmasters, who grant Facebook space on their websites by implementing buttons,

also cannot directly see how their content is being discussed inside the platform.

Facebook only enables very controlled flows of data back to webmasters in their

analytics, such as the impressions and referrals of their content in the news feed,

ticker and timeline and basic, anonymised demographic user data.

In order to extend its data mining and to become the central hub of social

linking – which refers to linking practices enabled through social buttons, Facebook is

reversely dependent on the dynamics of decentralisation as discussed above. Only

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because the platform can expand some of its key features into the entire web and

decentralise web content and the discussion thereof, it can also recentralise and

monetise the created data flows and links, as they all direct back to Facebook. The

dynamics of de- and recentralisation are not only interconnected, they also are a

prerequisite for the Like economy. They enable Facebook to maximise its data mining

activities while at the same time keeping control over the key entities of exchange ‒

data, links, traffic and ‒ as will be shown in the following section ‒ user affects.

4. A web economy of metrification and intensification

The social web that Facebook attempts to create is not only directed at enabling social

interactivity, but also at the production and circulation of data. Improving the ‘social

experience’ of the web functions as a vehicle for other objectives such as widening

data mining practices and profiling users. We will now explore how the Like

economy is creating a data-intensive infrastructure in which social interactivity and

engagement with web content is instantly turned into standardised and quantified

metrics and at the same time multiplied and intensified.

The Like button transforms users’ affective, spontaneous responses to web

content into quanta of numbers on the Like counter. It provides a one-click shortcut to

express a variety of affective responses such as excitement, agreement, compassion,

understanding, but also ironic and parodist liking. Such affects (Massumi, 2002) are

not measurable, countable and comparable as such, but are rather intensive in the

sense of DeLanda (2006), referring to transforming states of being. By asking users to

express various affective reactions to web content in the form of a click on a Like

button, these intensities can be transformed into a mere number on the Like counter

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and made comparable. Users can materialise their affective responses while Facebook

can count and monitor them at the same time.

While the Like button collapses a variety of mainly positive affective responses,

the Like counter combines even further activities such as commenting, sending and

sharing into the same metric, since the like is designed as a composite metric as

described above. These different forms of engagement with web content are regulated

in the Open Graph protocol, which enables particular reactions and activities rather

than others. Following Galloway, protocol might not directly produce actions but sets

out the conditions and dispositions for actors to act and interact (2004). In this sense,

social buttons both prestructure and enable the possibilities of expressing affective

responses to or engaging with web content, while at the same time measuring and

aggregating them. Additionally, as Like buttons also function as cookies, they

instantly collect data about button impressions and browsing behaviour which further

qualify content engagement.

The generated data flows are used in a twofold way. Firstly, to enrich

Facebook’s database and to cluster users for personalised advertising as discussed

before. Secondly, to show users what their contacts have liked or have engaged with

on the web as recommendations within the platform. On top of that the data is also

used in social plugins on external websites in order to foster even more engagement

and constantly generate more user data.

The quanta of data produced are not just metrifications of intensities, they also

have intensive capacities themselves. Facebook advertises the external Like button as

generator of traffic and engagement (Facebook + Media, 2010). Likers, the platform

argues, are more connected and active than average Facebook users. Each click on a

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Like button is supposed to lead to more traffic for, and more engagement with, web

content, as friends of likers are likely to follow their contacts’ recommendations or

will be influenced by what their friends like. With social plugins, Facebook is

enabling the systematic exposure of web engagement to a users’ network of friends:

they can immediately see which contacts have engaged with the page and the content

they have recommended or discussed. In order to reach potential likers and to create

ongoing liking dynamics, Facebook is recommending the implementation of several

plugins that directly show the activities of a user’s contacts (Facebook + Media,

2010). Each like can potentially generate more likes, shares and comments when

exposed to a particular social formation of Facebook friends and can therefore be

considered as scalable. In this way, the Like button not only enables the

materialisation and metrification of affective responses - it is designed to intensify

them as well.

This process of intensification is based on the creation of differently scaled

social formations to which acts of liking, sharing and commenting are being exposed.

While the Like counter shows the anonymous number of all likers and sharers,

detached from personal profiles, the majority of social plugins only depict the

activities of a users’ contact and thus will look different for each visitor. Depending

on their Facebook privacy settings, the activity of liking may be visible to everyone,

to all friends or a selected group of friends and is further distributed across their

timeline, the news feed and their ticker, creating threefold impression statistics for

webmasters. If a friend responds to a like with another like or a comment, this activity

is again made visible in new spaces, creating ever more social formations. Each

device of the Like economy is creating differently scaled social assemblages in

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DeLanda’s sense (2006), formations of users that are not stable but subject to change.

The data flows between profiles, the exposure on walls and the privacy settings allow

to scale up these formations to almost every web user or scale down to a selected few

Facebook friends. The engagement with web content within Facebook is thus not only

decentralised across a variety of news walls and tickers, but is also spread across a

multiplicity of social formations of different scales. Facebook actively encourages

users to control the content they are exposed to: users can now subscribe or

unsubscribe to activities of their friends or other public profiles, choosing between all-

and top stories. In addition to that, Facebook has introduced the close friends list

whose activities are featured more prominently, weighting particular friends stronger

than others. This weighting also feeds back into the increasingly differentiated news

feed, which consists of top stories, regular stories and the fast changing ticker. What

appears in these spaces is regulated by the Graph Rank, an algorithm that gives

prominence to activities of selected contacts or subscriptions that have generated

many activities in order to multiply this engagement even further.

Such dynamics of intensification show that engagement with the Like economy

is designed as an ongoing and potentially scalable process. In this framework, a like is

always more than one. The value of a single like lies both in the present and in the

future, in the +1 it adds to the Like counter and the number of x potential likes,

comments or shares it might generate later. If a like has to be understood as ≥ 1, the

Like economy creates a system in which surplus and value creation is gradually

situated in the future. The Like button on the one hand metrifies a number of affective

responses into a comparable metric, but the Open Graph exposes these quanta of data

to carefully selected social formations in which they are supposed to be contagious, to

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evoke further likes or shares. The infrastructure of the Like economy therewith

creates a particular relation between individual user activities and referent social

formations in which liking becomes scalable and quantification becomes productive

or more than representational (Thrift 2008). Different from in the informational web

which was structured by the universal Google’s PageRank algorithm, Facebook is not

aiming for such universal ranking through liking. It is creating multiple rankings and a

recommendation system based on weighted personal contacts, in which users do not

have to search for content, but content is presented to them.

Yet, it is not only user affects which are being scaled and intensified, the Like

economy also contributes to an increasing cross-syndication of content. As mentioned

above, with each like or share, web content is being syndicated to different news

feeds, topstories, tickers and user walls within the platform. As discussed in regard to

the decentralisation of web content and its engagement, the potential scalability of

liking also renders cross-syndication more scalable. Unlike the concept of the

informational web, which follows the idea that content is being produced in order to

be found, in the social web content is created to be shared, distributed and cross-

syndicated.

The relationship between economic value and the social in the case of Facebook

is based on the ongoing measurement, calculation and scalability of affect and

interactivity. It is only the traceable social that matters to Facebook, as the still

intensive, non-measurable, non-visible social is of no actual value for the company: it

can neither enter data mining processes nor be scaled up further. Therewith,

Zuckerberg’s claim that in the future economies will be organised by the social, is

rested on collapsing the social with the quantifiable and traceable. Being social online

22

means being traced and contributing to value creation for multiple actors like

Facebook and external webmasters.

The ongoing measurement of affect and engagement is directed at creating a

system of recommendation, which projects what users might like based on their

friend’s preferences. Just as the social is collapsed with the traceable, the metrifying

capacities of the Like button are inextricable from their intensifying capacities. Within

the Like economy, data and numbers have both performative and productive

capacities, they can generate user affects, enact more activities and thus multiply

themselves. To put it with Simondon: ‘Beyond information as quantity and

information as quality, there is what one could call information as intensity’ (cited in

Venn, 2010: 146). These dynamics are enabled through the medium-specific

infrastructure of the Like economy, which has to be understood and analysed as an

agent as well, as it makes the connection between social interactivity and economic

value possible.

5. The limits of sociality in Facebook’s social web

Corporate interest in social interactivity and user affects, as well as the collapse of the

social into the traceable, are not new to Facebook, but have to be understood in the

trajectory of post-Fordist economies, corporate interest in transactional online data as

well as attempts to objectify consumer affects (Arvidsson, 2011). Post-Fordist and

knowledge intensive economies have witnessed an increasing blurring of life and

labour, of social interactivity and the production of value. In the informational web,

user preferences and basic activities could be read from server log files, used to derive

engagement measures such as hits and time spent on a page. With the rise of the

23

social web, companies realised that everyday online activities provide a rich source of

information about user preferences, activities and affects that had previously only

been available through market research techniques – so-called transactional data. An

increasing range of social media monitoring services is currently tracking and

analysing user behaviour online, instantly turning social activity and web engagement

into different quanta of data (Lury and Moor, 2010). Special attention is being paid to

sentiment, the positive, negative or neutral relation users have to topics or web objects

in order to forecast potential consumption (Arvidsson, 2011). In this sense,

Facebook’s endeavours are not new and have to be accounted for in the context of

corporate social media monitoring. However, the Like economy creates an

infrastructure that not only allows to mine transactional data instantly but also to

multiply it.

Throughout the paper we have approached this development from a medium-specific

perspective by discussing how web native devices such as social buttons and the Open

Graph have contributed to this collapse of the social and the traceable. Comparing the

emerging Like economy with the hit and link economy, we have explored how the

launch of social buttons has reintroduced the role of users in organising web content

and the fabric of the web – and how the infrastructure of the Open Graph has turned

user affects and engagement into both data and objects of exchange. Starting from

Zuckerberg’s vision, in which the so-called social will be the future organising

principle of economies, we have shown that in the case of Facebook this has been

accomplished by collapsing the social with the quantifiable and traceable. Being

social online means producing surplus value for Facebook. We have presented a

twofold analysis of the Like economy. First, by showing how it is creating a particular

24

fabric of the web through social buttons, in which the platform and external websites

are at the same time de- and recentralised. Decentralised, as the Like economy fosters

increasing data flows and cross-syndication of content. Re-centralised, as the fabric of

the web is no longer created between webmasters, but always links back to Facebook

who also regulates the access to the created data flows for the different actors

involved. Second, by following the medium-specific perspective further, we have

drawn attention to the capacity of the Like button to both metrify and intensify user

affect and engagement. It can be argued that the Like economy is creating a digital

space of ongoing measurement and multiplication of user affect and interactivity.

User engagement online has so far often been discussed in a post-Marxist

terminology of labour, production and user exploitation. The involvement of web

users in the production of social media platforms has been understood as a form of

social production (Scholz and Hartzog, 2010), as prosumption or working

consumers/users (Fuchs, 2010) or as free labour (Terranova, 2004) in which

consumers voluntarily engage in productive activities without financial reward. The

medium specific perspective offers a complementary account, drawing attention to the

role of devices. Instead of thinking of user engagement as labour, we have suggested

that the Like button and the Open Graph constantly turn life itself, including affects

and social relations, into countable and exchangeable entities of data which ideally

multiply themselves. Affect and social proximity are not valuable per se, as they are

intensive, hard to measure and to compare. It is the medium-specific infrastructure of

the Like economy that allows their transformation into quantified likes, which can

then enter multiple forms of exchange: from producing data for user mining and

patterning, to creating recommendation traffic from Facebook, getting access to Like

25

button statistics or moving behind the Likewall.

To conclude, we will return to Zuckerberg’s vision that in the future, the social

will become the key organising principle of economies. Facebook is claiming to

integrate more social activities into its platform, as former employee Matt Cohler

explains: ‘Facebook has always thought that anything that is social in the world

should be social online’ (Gelles, 2010) – a claim that becomes particularly vivid in

relation to the new apps which allow for the documentation of any potential action.

But there are limits to Facebook’s enclosure of sociality, most notably in the

current absence of the widely requested Dislike button as a critical counterpart to the

Like button. Despite increasing user demand, Facebook has not shown any intentions

to implement a Dislike button feature. Although such a button might comply with

corporate interest in both positive and negative sentiment and might – just as the Like

button – simultaneously metrify and intensify negative affective responses, Facebook

abstains from its implementation. The platform officially claims that a one-click

solution for negative affect might lead to insensitive use (Sawens, 2010). Disliking

has further been blocked from becoming part of the action apps, making sure

developers will not introduce the Dislike feature indirectly.

Yet, the decision to abstain from ‘disliking’ also bears economic dimensions

as traffic and engagement generated through Like buttons can be considered as

positive impressions and activities. Clicks on the Like button function as

recommendations of external web content and thus are likely to create further positive

reactions or site impressions, all adding to the external Like counter as measure of

positive or at least neutral engagement. Opening up the possibility of a one-click

solution for critique or controversial linking could potentially create negative traffic,

26

which cannot be collapsed in the composite Like counter and might not be desired by

webmasters. The Like economy is facilitating a web of positive sentiment in which

users are constantly prompted to like, enjoy, recommend and buy as opposed to

discuss or critique - making all forms of engagement more comparable but also more

sellable to web masters, brands and advertisers who are less interested in getting

critical or negative referral traffic from Facebook. The Open Graph protocol

prestructures the metrification and intensification of recommendations while critique

and disliking remain rather intensive and non-measurable. Hence, while Facebook

claims that it aims to turn any social engagement into a part of its platform, the

absence of negative affects has until the autumn of 2011 marked the limits of

Facebook’s understanding of sociality. The introduction of the activity apps has

complicated the affective space of Facebook, allowing for differentiated and even

negative activities in relation to web objects, such as to hate, disagree and criticise -

while the action “dislike” remains blocked. Yet, such activities cannot be performed

on potentially any web objects, as liking allows, but are limited to the objects defined

by the developers.

The Like economy has thus created an infrastructure that comes across as

facilitating a more social web experience, but it only enables particular forms of social

engagement and affective responses through its protocol, collapsing the social with

the traceable and marketable and filtering it for positive affects.

Notes

1. It could even be argued that the social buttons took up the empty space created

by search engines when they rendered the commentspace worthless through

27

the ‘nofollow’ attribute (Weltevrede, 2011), as social media create distributed

commentspaces and allow for engaging with content outside of the website.

References

Appelquist D, Brickley D, Carvahlo M, et al. (2010) A Standards-based, Open and Privacy-aware Social Web. W3C Incubator Group Report. Available at: http://www.w3.org/2005/Incubator/socialweb/XGR-socialweb-20101206/ (accessed 18 October 2011).

Arvidsson A (2011) General Sentiment - How Value and Affect Converge in the Information Economy. Social Science Research network. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1815031 (accessed 18 October 2011).

Beer D (2009) Power through the algorithm? Participatory web cultures and the technological unconscious. New Media & Society 11(6): 985-1002.

Berners-Lee T (2010) Long Live the Web: A Call for Continued Open Standards and Neutrality. Scientific American. Available at: http://www.scientificamerican.com/article.cfm?id=long-live-the-web&print=true (accessed 4 May 2011).

boyd d (2010) Social Network Sites as Networked Publics: Affordances, Dynamics, and Implications. In: Papacharissi Z (ed) Networked Self: Identity, Community, and Culture on Social Network Sites. New York: Routledge, 39-58.

Cutts M and Shellen J (2005) Preventing comment spam. The Official Google Blog, 18 January. Available at: http://googleblog.blogspot.com/2005/01/preventing-comment-spam.html (accessed 4 May 2011).

D'Alessio D (1997) Use of the World Wide Web in the 1996 US election. Electoral Studies 16(4): 489-500.

DeLanda M (2006) New Philosophy of Society: Assemblage Theory and Social Complexity. London: Continuum.

Facebook + Media (2010) The Value of a Liker. Available at: https://www.facebook.com/note.php?note_id=150630338305797 (accessed 17 October 2011).

Facebook Developers (2011) Graph API. Available at: https://developers.facebook.com/docs/reference/api/ (accessed 17 October 2011).

Facebook Statistics (2011) Available at: https://www.facebook.com/press/info.php?statistics

28

(accessed 17 October 2011).

Fuchs C (2010) Labor in Informational Capitalism and on the Internet. The Information Society 26(3): 179-196.

Galloway A (2004) Protocol: How Control Exists after Decentralization. Cambridge, MA: The MIT Press.

Gelles D (2010) Facebook’s grand plan for the future. FT Magazine, 3 December. Available at: http://www.ft.com/intl/cms/s/2/57933bb8-fcd9-11df-ae2d-00144feab49a.html#axzz1ayaHS1Vs (accessed 15 October 2011).

Gibson D, Kleinberg J and Raghavan P (1998) Inferring Web Communities From Link Topology. HYPERTEXT ’98. Proceedings Of the Ninth ACM Conference On Hypertext and Hypermedia: Links, Objects, Time and Space: 225–234.

Kinsey M (2009) Keeping Count Of Sharing Across the Web. The Facebook Blog, 26 October. Available at: http://blog.facebook.com/blog.php?post=165161437130 (accessed 16 January 2011).

Lury C and Moor L (2010) Brand Valuation and Topological Culture. In: Aronczyk M and Powers D (eds) Blowing up the Brand. New York: Peter Lang, 29-52.

MacManus R (2011) The Pros & Cons of Frictionless Sharing. ReadWriteWeb. 28 September. Available at: http://www.readwriteweb.com/archives/frictionless_sharing_pros_cons.php (accessed 12 October 2011).

Massumi B (2002) Parables for the Virtual: Movement, Affect, Sensation. Durham, N.C: Duke University Press Books.

Page L, Brin S, Motwani R and Winograd T (1999) The PageRank citation ranking: Bringing order to the web. Stanford InfoLab. Available at: http://ilpubs.stanford.edu:8090/422 (accessed 16 October 2011).

Pearlman L (2009) I Like This. The Facebook Blog, 10 February. Available at: http://blog.facebook.com/blog.php?post=53024537130 (accessed 16 January 2011).

Rogers R (2002) Operating Issue Networks On The Web. Science as Culture 11(2): 191-213.

Rogers R (2005) Old and New Media: Competition and Political Space. Theory & Event 8(2).

Rogers R (2012 forthcoming) Digital Methods. Cambridge, MA: MIT Press.

Roosendaal A (2010) Facebook Tracks and Traces Everyone: Like This! Social Science Research network. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1717563 (accessed 16 January 2011).

Ross J (2009) John Hagel on The Social Web. OʼReilly Radar, 24 October. Available at: http://radar.oreilly.com/2009/10/john-hagel-on-the-social-web.html (accessed 5 May

29

2011).

Sabbagh D (2011) Google evangelist warns Facebook could be the next AOL or IBM. The Guardian, 21 September. Available at: http://www.guardian.co.uk/technology/2011/sep/21/google-facebook-vint-cerf (accessed 18 October 2011).

Sawens P (2010) Facebook Dislike Button: Why it Will Never Happen. The Next Web, 10 October. Available at: http://thenextweb.com/socialmedia/2010/10/10/facebook-dislike-button-why-it-will-never-happen (accessed 18 October 2011).

Scholz T and Hartzog P ((n.d.)) Toward a critique of the social web. Re-public: re-imagining democracy. Available at: http://www.re-public.gr/en/?p=201 (accessed 18 October 2011).

Siegler MG (2011) Facebook Also Quietly Rolled Out A Like Button Chrome Extension. Techcrunch, 1 September. Available at: http://techcrunch.com/2011/09/01/facebook-chrome-like-button/ (accessed 16 October 2011).

Terranova T (2004) Network Culture: Politics for the Information Age. London: Pluto Press.

Thrift N (2008) Non-Representational Theory: Space, Politics, Affect. London: Routledge.

Venn C (2010) Individuation, Relationality, Affect: Rethinking the Human in Relation to the Living. Body & Society 16(1): 129 -161.

Walker J (2002) Links and Power: The Political Economy Of Linking On the Web. HYPERTEXT ’02. Proceedings Of the Thirteenth ACM Conference On Hypertext and Hypermedia: 72–73.

Wesh M (2007) Information R/evolution. YouTube. Available at: http://www.youtube.com/watch?v=-4CV05HyAbM (accessed 4 May 2011).

Weltevrede E (2011) Conference panel discussion. Digital Methods Initiative Winter Conference 2011, University of Amsterdam.

Zuckerberg M (2011) Building the Social Web Together. The Facebook blog, 21 April. Available at: https://blog.facebook.com/blog.php?post=383404517130 (accessed 16 October 2011).